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Open Food Facts

domdomegg Scanned 2d ago

Search and contribute to the Open Food Facts database.

B
84.7 / 100

Versions

1.0.0latest
first seen Jun 5, 2026
2.5.1
first seen May 19, 2026
PermissionsTool SafetyAuthAnnotationsCode QualityStabilitySpecVuln HistoryAuthorTransparencyCommunity

Tools 10

upload_image
annotations: verified low

Upload a product image to Open Food Facts. Requires OFF_USER_ID and OFF_PASSWORD.\n\nPrefer more photos over fewer. Panels with text (ingredients, nutrition, certifications, recycling instructions) are highest value as OFF can OCR them. Plain sides with just a colour or logo are lowest value but still worth uploading if you have them.\n\nUse the most appropriate imagefield (front, ingredients, nutrition, packaging). Use "other" for additional photos — this uploads without selecting the image as a display image, which is useful when a good display image already exists or for supplementary angles.\n\nThe OFF server auto-selects images for front/nutrition/ingredients/packaging on upload unless one is already selected. If you get "status not ok" but a positive imgid, the image uploaded successfully but was not selected (e.g. a display image already exists).\n\nFor images on disk, base64-encode them first (e.g. via shell: `base64 -i photo.jpg`).

readOnlyHint false
get_api_docs
annotations: verified low

Get Open Food Facts API documentation. Useful for understanding available endpoints before using call_api.

readOnlyHint true
get_skill
annotations: verified low

Get the OFF upload skill document. This describes the recommended process for bulk uploading food packaging photos to Open Food Facts.

readOnlyHint true
add_or_edit_product
annotations: verified low

Add a new product or edit an existing one on Open Food Facts. Requires OFF_USER_ID and OFF_PASSWORD. The more fields you fill, the more useful the entry. At minimum provide product_name, brands, and categories — these feed the search index, and a sparse entry won't be findable. If you have a photo of the pack, transcribe everything you can read: ingredients, nutrition, origins, traceability stamps, recycling icons, certifications. Fields that drive derived data: - ingredients_text → allergens, additives, NOVA group, fruit/veg % - nutrition + categories → Nutri-Score - packagings + origins → Eco-Score - product_name + brands + categories + labels → search _keywords Pitfalls learned the hard way: - Free-text packaging shapes get fuzzy-matched against the taxonomy. "Pouch" resolves to "en:pouch-flask" (a stand-up spouted pouch). Use taxonomy IDs like "en:bag" or "en:individual-bag" instead. - OFF has no generic "pouch" shape in its taxonomy. For vacuum-sealed individual portions use "en:individual-bag"; for plastic film wrap use "en:film". Recommended workflow for adding a product from photos: 1. Check if product exists with get_product first to avoid overwriting good data 2. Upload photos with upload_image. Prefer more photos over fewer — panels with text (ingredients, nutrition, certifications, recycling instructions) are highest value as OFF can OCR them. Plain sides with just a colour or logo are lowest value but still worth uploading if you have them. Use the most appropriate imagefield (front, ingredients, nutrition, packaging) and "other" for the rest. 3. Call this tool with all fields you can read from the photos. Set both quantity and serving_size. 4. Set packagings_complete: true only when all packaging components are listed For products with only prepared nutrition (jelly mixes, powdered drinks, etc.), use nutrition_prepared instead of nutrition. For values printed as "< 0.5g" on the packet, pass the string "< 0.5" — the less-than modifier will be preserved. Nutrition fields mirror the label columns: nutrition (per 100g as sold), nutrition_per_serving (per serving as sold), nutrition_prepared (per 100g prepared), nutrition_prepared_per_serving (per serving prepared). OFF auto-derives per-serving from per-100g + serving_size, so nutrition_per_serving is only needed when the label shows explicit per-serving values you want to preserve.

readOnlyHint false
get_product
annotations: verified low

Get product information from Open Food Facts by barcode. Reads the primary database directly (no sync lag), so this is always current even when search_products returns stale results. Prefer this over search whenever you have a barcode. If this returns "product not found", the product genuinely isn\'t in the database — you can add it with add_or_edit_product.

readOnlyHint true
search_products_standard
annotations: verified low

Search Open Food Facts with structured filters. Best for simple keyword queries and brand/category filtering. Returns exact result counts and well-populated products. If you have a barcode, use get_product instead. How search works: strict AND against a keyword index built from product_name, generic_name, brands, categories, origins, labels. One unmatched query word → zero results. Tips: - Prefer 2-3 distinctive words over the full product name - Put brand names in brands_tags, not the query text - Brand normalization is generous: "sainsburys", "sainsbury's", "sainsbury-s" all match - For fresh produce, use brands_tags + categories_tags rather than text search - sort_by=popularity works well here (not supported in search_products_lucene) If you get zero results, try dropping words or using search_products_lucene which has more flexible text matching.

readOnlyHint true
search_products_lucene
annotations: verified low

Search Open Food Facts using the Search-a-licious Elasticsearch backend. Powered by Lucene query syntax with full boolean logic and negation support. Use this instead of search_products_standard when you need: - Negation queries: find gluten-free cereals with allergens_tags_without="en:gluten" - Filter-only browsing: categories_tags without any text query (standard API times out on this) - Combined text + filter with relevance scoring: text matches are ranked by relevance within filter results - Boolean logic in raw Lucene: brands:"kellogg*" OR brands:"nestle" Trade-offs vs search_products_standard: - Counts are approximate (capped at 10,000 for large result sets) - Brand tag matching may be narrower (less normalization than standard) - Data has a short sync delay (hours) from the primary database - popularity sort uses scan counts rather than the standard popularity algorithm Response format matches search_products_standard: { count, page, page_size, page_count, products: [...] }

readOnlyHint true
autocomplete
annotations: verified low

Get autocomplete suggestions for Open Food Facts taxonomy entries (brands, categories, labels, etc.).

readOnlyHint true
call_api
annotations: verified low

Make a direct call to any Open Food Facts API endpoint. Use get_api_docs to see available endpoints. Auth credentials are included automatically for write operations if configured. Two body modes for writes: - params: form-encoded (for /cgi/*.pl and /api/v2/* legacy endpoints) - json_body: raw JSON (for /api/v3/* endpoints — required for structured fields like packagings) Example v3 packagings write: method: PATCH endpoint: /api/v3/product/0123456789012 json_body: {"fields":"packagings","product":{"packagings":[{"number_of_units":1,"shape":{"id":"en:bag"},"material":{"id":"en:plastic"},"recycling":{"id":"en:recycle"}}]}} WARNING: Do NOT use old-style prepared nutrition params like nutriment_fat_prepared — they have a known server bug that stores data incorrectly. Use new-style params instead: nutrition_input_sets_prepared_100g_nutrients_fat_value_string=0.5

readOnlyHint false
select_image
annotations: verified low

Select, crop, and rotate a previously uploaded product image on Open Food Facts. Requires OFF_USER_ID and OFF_PASSWORD.

readOnlyHint false

Permissions 2

network medium
Server uses network capabilities via: fetch()
env_vars low
Server uses env_vars capabilities via: process.env

Scan Findings 33

info
Transport: stdio manifest_parser · 90%
info
Required env vars (7) manifest_parser · 80%
info
Sandbox failed to start for output poisoning scan output_poisoning · 100%
info
Tool 'upload_image' annotations are consistent annotation_checker · 80%
info
Tool 'get_api_docs' annotations are consistent annotation_checker · 80%
info
Tool 'get_skill' annotations are consistent annotation_checker · 80%
info
Tool 'add_or_edit_product' annotations are consistent annotation_checker · 80%
info
Tool 'get_product' annotations are consistent annotation_checker · 80%
info
Tool 'search_products_standard' annotations are consistent annotation_checker · 80%
info
Tool 'search_products_lucene' annotations are consistent annotation_checker · 80%
info
Tool 'autocomplete' annotations are consistent annotation_checker · 80%
info
Tool 'call_api' annotations are consistent annotation_checker · 80%
info
Tool 'select_image' annotations are consistent annotation_checker · 80%
high
Remote transport without authentication auth_checker · 70%
info
Sandbox failed to start for behavioral verification behavioral_verifier · 100%
medium
Vulnerable dependency: @modelcontextprotocol/sdk@1.24.0 (GHSA-345p-7cg4-v4c7) dependency_analyzer · 95%
medium
Vulnerable dependency: @modelcontextprotocol/sdk@1.24.0 (GHSA-8r9q-7v3j-jr4g) dependency_analyzer · 95%
medium
Buffer.from base64 in domdomegg-openfoodfacts-mcp-6657761/src/tools/upload-image.ts:39 entropy_analyzer · 75%
info
package.json metadata manifest_parser · 100%
medium
Permission: network access detected permission_analyzer · 70%
low
Permission: env_vars access detected permission_analyzer · 90%
info
SBOM generated: 398 components sbom_generator · 100%
info
Tool: upload_image manifest_parser · 85%
info
Tool: get_api_docs manifest_parser · 85%
info
Tool: get_skill manifest_parser · 85%
info
Tool: add_or_edit_product manifest_parser · 85%
info
Tool: get_product manifest_parser · 85%
info
Tool: search_products_standard manifest_parser · 85%
info
Tool: search_products_lucene manifest_parser · 85%
info
Tool: autocomplete manifest_parser · 85%
info
Tool: call_api manifest_parser · 85%
info
Tool: select_image manifest_parser · 85%
medium
No build provenance detected (SLSA L0) slsa_assessor · 90%